Summary

In this chapter, we discussed various methods for modeling univariate time series data from stationary time series models such as ARMA to non-stationary models such as ARIMA. We started with stationary models and discussed how to identify modeling approaches based on the characteristics of time series. Then we built on the stationary models by adding a term in the model to stationarize time series. Finally, we talked about seasonality and how to account for seasonality in an ARIMA model. While these methods are powerful for forecasting, they do not incorporate potential information from other external variables. As in the previous chapter, we will see that external variables can help improve forecasts. In the next chapter, we will look at multivariate methods for time series data to take advantage of other explanatory variables.